It is often of interest to locate and characterize tumors in a non-invasive manner, this is especially relevant for brain tumors (gliomas) due to their inherent inaccessibility. Here, MRI (Magnetic Resonance Imaging) and CT (Computed tomography) imaging are typically the imaging methods of choice. While being excellent for determining position and size, these techniques convey little information about the functional status of the tumor tissue or adjacent tissue (e.g. degree of angiogenesis, tissue viability, malignancy, etc). Although tumor malignancy to some extent may be suggested indirectly by contrast enhanced imaging, several studies have shown that the degree of contrast enhancement is by no means a reliable indicator of tumor grade. Based on these shortcomings, dynamic susceptibility perfusion imaging is becoming increasingly important due to its usefulness in physiological imaging.
Perfusion imaging of tumors is used to demonstrate the vascular growth (angiogenesis and neovascularization) associated with tumor growth by imaging the Blood Volume (BV) or Blood Flow (BF) in a tumor. Since BV values correlate with the grade of vascularity; high-grade (malign) tumors tend to have higher BV values than low-grade (less malign) tumors. Perfusion imaging is therefore helpful in the grading of tumors. Imaging methods for mapping the cellular metabolism may correlate with the grade of vascularity in a similar way, and may therefore also be used.
Several studies have shown to differentiate between high- and low-grade gliomas based on relative cerebral blood volume (rCBV) maps obtained by perfusion MRI. The general way to characterize glioma malignancy is by measuring the ratio between the most elevated rCBV area within the glioma (“hot-spot”), and an unaffected contra-lateral white matter rCBV value. Although several notations are used, this ratio is often referred to as normalized CBV (nCBV), and high-grade gliomas tend to have a higher nCBV ratio than low-grade gliomas. This method is described in e.g. Wetzel et al., Radiology 2002: 224: 797-803.
However, there are several limitations to this method. First, the selection of glioma hot-spot is highly user-dependent and differentiation between vessels and tumor region of true blood volume elevation can be challenging and a source of error. Secondly, since only a few image pixels are typically used to determine the rCBV hot-spot, the method is inherently sensitive to image noise and other sources of spurious pixel values (e.g. spikes introduced by the algorithms used to generate the nCBV maps). Thirdly, unaffected white matter rCBV values are generally used to derive the nCBV value. This is based on the assumption that most gliomas are located in white matter. However, incorrect selection of reference rCBV values might result in either under- or overestimation of nCBV values. Finally, oligodendrogliomas tend to give high nCBV values irrespective of glioma grade.
As a result, cut-off nCBV values between high-grade and low-grade gliomas might be harder to establish if oligodendrogliomas are included. As an alternative to the hot-spot method, Schmainda et al, Am J Neuroradiol 2004: 25: 1524-1532, suggests to use the mean nCBV for the tumor as the basis for grading (referred to as the WT method), but this method has not been consistently compared to the hot-spot method. Also, a mean nCBV has the disadvantage of not reflecting the diversity of values in the tumor.